/**
 * 模板匹配
 * 1. matchTemplate(img_tmpl, tmpl, (mask))：搜索匹配
 * 2. minMaxLoc()：求数组最大，最小值的位置（根据算法可能是最大匹配或最小匹配，但是不一定是极值匹配）
 */

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace std;
using namespace cv;

//! [declare]
/// Global Variables
bool use_mask;
Mat img;
Mat templ;
Mat mask;
Mat result;
const char *image_window = "Source Image";
const char *result_window = "Result window";

int match_method;
int max_Trackbar = 5;
//! [declare]

/// Function Headers
void MatchingMethod(int, void *);

int main(int argc, char *argv[]) {
    if (argc < 3) {
        cout << "Not enough parameters" << endl;
        cout << "Usage:\n" << argv[0] << " <image_name> <template_name> [<mask_name>]" << endl;
        return -1;
    }

    //! [load_image]
    /// Load image and template
    img = imread(argv[1], IMREAD_COLOR);
    templ = imread(argv[2], IMREAD_COLOR);

    if (argc > 3) {
        use_mask = true;
        mask = imread(argv[3], IMREAD_COLOR);
    }

    if (img.empty() || templ.empty() || (use_mask && mask.empty())) {
        cout << "Can't read one of the images" << endl;
        return EXIT_FAILURE;
    }
    //! [load_image]

    //! [create_windows]
    /// Create windows
    namedWindow(image_window, WINDOW_AUTOSIZE);
    namedWindow(result_window, WINDOW_AUTOSIZE);
    //! [create_windows]

    //! [create_trackbar]
    /// Create Trackbar
    const char *trackbar_label = "Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED";
    createTrackbar(trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod);
    //! [create_trackbar]

    MatchingMethod(0, 0);

    //! [wait_key]
    waitKey(0);
    return EXIT_SUCCESS;
    //! [wait_key]
}

/**
 * @function MatchingMethod
 * @brief Trackbar callback
 */
void MatchingMethod(int, void *) {
    //! [copy_source]
    /// Source image to display
    Mat img_display;
    img.copyTo(img_display);
    //! [copy_source]

    //! [create_result_matrix]
    /// Create the result matrix
    int result_cols = img.cols - templ.cols + 1;
    int result_rows = img.rows - templ.rows + 1;

    result.create(result_rows, result_cols, CV_32FC1);
    //! [create_result_matrix]

    //! [match_template]
    /// Do the Matching and Normalize
    bool method_accepts_mask = (TM_SQDIFF == match_method || match_method == TM_CCORR_NORMED);
    if (use_mask && method_accepts_mask) { matchTemplate(img, templ, result, match_method, mask); }
    else { matchTemplate(img, templ, result, match_method); }
    //! [match_template]

    //! [normalize]
    normalize(result, result, 0, 1, NORM_MINMAX, -1, Mat());
    //! [normalize]

    //! [best_match]
    /// Localizing the best match with minMaxLoc
    double minVal;
    double maxVal;
    Point minLoc;
    Point maxLoc;
    Point matchLoc;

    minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, Mat());
    //! [best_match]

    //! [match_loc]
    /// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
    if (match_method == TM_SQDIFF || match_method == TM_SQDIFF_NORMED) { matchLoc = minLoc; }
    else { matchLoc = maxLoc; }
    //! [match_loc]

    //! [imshow]
    /// Show me what you got
    rectangle(img_display, matchLoc, Point(matchLoc.x + templ.cols, matchLoc.y + templ.rows), Scalar::all(0), 2, 8, 0);
    rectangle(result, matchLoc, Point(matchLoc.x + templ.cols, matchLoc.y + templ.rows), Scalar::all(0), 2, 8, 0);

    imshow(image_window, img_display);
    imshow(result_window, result);
    //! [imshow]

    return;
}
